Learning sequential tasks is inevitably necessary for the
development of cognitive systems, may it be navigation,
planning or communication with language and gestures. Deep
neural architectures do not possess an explicit time resolution
to capture time correlation nor timescales inherent in sentences
and actions. A popular technique to overcome the missing
temporal link in, e.g., CNNs is to use 3D convolution kernels
for image stacks representing videos. However, the benefit of
such kernels on the performance compared to the standard 2D
kernel is not yet satisfiable answered [1]}.
Due to the inherent correlations in sequential tasks, Re-
current Neural Networks (RNN) are a widely used tool as
they provide local network memory and are thus able to
compute different time aspects (e.g. continuous-time RNN).
RNNs in their diverse implementations were successfully used
in cognitive robotic scenarios (see e.g. [2]}, [3]}). Due to
the error computations propagated through the whole network
using gradient descent, RNN training suffers from vanishing or
exploding gradients, trapping into local minima, and network
bifurcations. To overcome these issues, RNNs need a lot of
retraining and are thus computationally demanding.
Alternatively, the Reservoir Computing (RC) paradigm in-
troduced a simplified training by the conceptual separation of
a high-dimensional reservoir of randomly connected neurons
providing rich input representations which are read out by
simple linear models and thus updating the network weights
becomes obsolete. Popular implementations are the Liquid
State Machines [4]} (LSM) and Echo State Networks [5]}
(ESN) the latter demonstrating competitive performance in
predictions tasks for (chaotic) timeseries [6]}. Despite the
reduced computational effort, only a few applications for
robots were developed until today. Some highlights in the
literature using the RC principles include navigation [7]}, [8]}
and language acquisition [9]}. Especially the latter research
shows that RC is a promising framework combining principles
from neuroscience and developmental psychology for the
highly complex cognitive task of learning and understanding
language. Also, only little is known about the potential of
RC algorithms for visual tasks. As images and videos are
highly complex in structure and introduce varying lighting
conditions and perspectives, a solution combining the robust
feature capabilities provided by DNNs with ESNs capturing
the inherent temporal structure showed good performance on
a gesture recognition task for a set of command gestures [10]}.
The coupling of DNNs with RC was also highlighted recently
regarding future research directions [11]}. Methods based on
RC can also be exploited for robot control [12]} and locomotion
[13]} but coupling RC with more complex robot scenarios is
still in its infancy.
We believe, that the area of cognitive robotics would sub-
stantially benefit from further research on the potentials of RC
frameworks complementing other computational methods like
DNNs for robot perception or even substituting existing RNN
methods exploiting the simplified yet robust training. Also,
analysis of ESNs regarding their "edge of stability" would
unify hypotheses on cognition underlying chaotic computa-
tions with performance maximization [14]}, which can lead to
effective network design.
